Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition
Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9004609/ |
| _version_ | 1819159056252665856 |
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| author | Junding Sun Yanan Lv Chaosheng Tang Haifeng Sima Xiaosheng Wu |
| author_facet | Junding Sun Yanan Lv Chaosheng Tang Haifeng Sima Xiaosheng Wu |
| author_sort | Junding Sun |
| collection | DOAJ |
| description | Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance. |
| first_indexed | 2024-12-22T16:34:29Z |
| format | Article |
| id | doaj.art-72ba5b93c4994623ba385a90fb5be27a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:34:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-72ba5b93c4994623ba385a90fb5be27a2022-12-21T18:19:59ZengIEEEIEEE Access2169-35362020-01-018357773579110.1109/ACCESS.2020.29753129004609Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave PartitionJunding Sun0https://orcid.org/0000-0001-7349-0248Yanan Lv1https://orcid.org/0000-0003-0670-4044Chaosheng Tang2https://orcid.org/0000-0001-6923-855XHaifeng Sima3https://orcid.org/0000-0002-2049-3637Xiaosheng Wu4https://orcid.org/0000-0003-1688-9564School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaFace recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance.https://ieeexplore.ieee.org/document/9004609/Face recognitionlocal gradient number patternfuzzy convex-concave partitionLDP-based methods |
| spellingShingle | Junding Sun Yanan Lv Chaosheng Tang Haifeng Sima Xiaosheng Wu Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition IEEE Access Face recognition local gradient number pattern fuzzy convex-concave partition LDP-based methods |
| title | Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition |
| title_full | Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition |
| title_fullStr | Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition |
| title_full_unstemmed | Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition |
| title_short | Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition |
| title_sort | face recognition based on local gradient number pattern and fuzzy convex concave partition |
| topic | Face recognition local gradient number pattern fuzzy convex-concave partition LDP-based methods |
| url | https://ieeexplore.ieee.org/document/9004609/ |
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